1

Design of an Artificial Decision Maker

for a Human-based Social Simulation

– Experience of the SimParc Project (Submitted to Cargese’09 Seminar)

Jean-Pierre Briot1,2

, Alessandro Sordoni1, Eurico Vasconcelos

2

Vinícius Sebba Patto1, Diana Adamatti

3, Marta Irving

4, Carlos Lucena

2

(1) Laboratoire d’Informatique de Paris 6, Université Pierre et Marie Curie – CNRS, Paris,

France

(2) Computer Science Department, Pontifícia Universidade Católica, Rio de Janeiro, RJ, Brazil

(3) Faculdade de Tecnologia (FTEC), Caxias do Sul, RS, Brazil

(4) EICOS Postgrad Program, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil

Corresponding author : Jean-Pierre.Briot@lip6.fr

Abstract

This paper addresses an ongoing experience in the design of an artificial agent

taking decisions in a social simulation (more precisely, a role playing game)

populated by human agents and by artificial agents. At first, we will present our

current research context, an ongoing research project aimed at computer-based

support for participatory management of protected areas (and more specifically

national parks) in order to promote biodiversity conservation and social inclusion.

Our applicative objective is to help various stakeholders (e.g., environmentalist

NGOs, communities, tourism operators, public agencies) to collectively understand

conflict dynamics for natural resources management and to explore negotiation

management strategies for the management of parks, one of the key issues linked to

biodiversity conservation. Our approach combines techniques such as: distributed

role playing games (serious games), geographical information systems, support for

negotiation between players, and insertion of various types of artificial agents

(virtual players, decision making agents, assistant agents). In this paper, we will

focus on the design of the decision making agent architecture for the park manager

agent, the rationales for his decision and how he takes into account the

preferences/votes from the players of the game and may justify/explain his decisions.

Keywords

Social simulation, serious games, participatory management, decision making, multi-

agent, negotiation, argumentation, explanation

1. Introduction

In this paper, we discuss the issue for an artificial agent to take decisions in a

social simulation. The type of social simulation that we refer to here is a social

simulation populated by humans. More precisely, we consider a role playing game

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(“serious game”) where humans play some role and discuss, negotiate and take

decisions about a common domain, in our case environment management decisions.

We are now in the process of inserting artificial agents into the human-based

social simulation [Briot et al. 2008]. One of the ideas is to possibly replace some of

the human players by artificial players (artificial agents). The social simulation will

therefore become hybrid, with human and artificial agents. A first motivation is to

address the possible absence of sufficient number of human players for a game

session [Adamatti et al. 2007]. But this will also allow more systematic experiments

about specific configurations of players profiles, because of artificial players’

objective, deterministic and reproducible behaviors.

More precisely, we are considering three types of artificial agents: artificial park

manager, artificial players and assistant agents. In this paper we focus on the design

of the artificial park manager agent. Its objective is to take decision based on its own

analysis of the situation and on the proposals by the players. The agent is also able to

explain its decision based on its chain of argumentation.

In this paper, after introducing the SimParc project, its role playing game and its

computer support, we describe the park manager agent objectives, architecture and

implementation.

2. The SimParc Project

2.1 Project Motivation

A significant challenge involved in biodiversity management is the management of

protected areas (e.g., national parks), which usually undergo various pressures on

resources, use and access, which results in many conflicts. This makes the issue of

conflict resolution a key issue for the participatory management of protected areas.

Methodologies intending to facilitate this process are being addressed via bottom-up

approaches that emphasize the role of local actors. Examples of social actors involved

in these conflicts are: park managers, local communities at the border area, tourism

operators, public agencies and NGOs. Examples of inherent conflicts connected with

biodiversity protection in the area are: irregular occupation, inadequate tourism

exploration, water pollution, environmental degradation and illegal use of natural

resources.

Our SimParc project focuses on participatory parks management. (The origin of

the name SimParc stands in French for “Simulation Participative de Parcs”) [Briot et

al. 2007]. It is based on the observation of several case studies in Brazil. However,

we chose not to reproduce exactly a real case, in order to leave the door open for

broader game possibilities [Irving et al. 2007]. Our project aim is to help various

stakeholders at collectively understand conflicts and negotiate strategies for handling

them.

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2.2. Approach

Our initial inspiration is the companion modeling (ComMod) approach about

participatory methods to support negotiation and decision-making for participatory

management of renewable resources [Barreteau et al. 2003]. Their pioneer method,

called MAS/RPG, consists in coupling multi-agent simulation (MAS) of the

environment resources with a role-playing game (RPG) [Barreteau 2003]. Hence,

stakeholders may understand and explore the consequences of their decisions. The

RPG acts like a “social laboratory”, because players of the game can try many

possibilities, without real consequences.

Recent works proposed further integration of role-playing into simulation, and the

insertion of artificial agents, as players or as assistants. Participatory simulation and

its incarnation, the Simulación framework [Guyot and Honiden 2006], focused on a

distributed support for role-playing and negotiation between human players. All

interactions are recorded for further analysis (thus opening the way to automated

acquisition of behavioral models) and assistant agents are provided to assist and

suggest strategies to the players. The Games and Multi-Agent-based Simulation

(GMABS) methodology focused on the integration of the game cycle with the

simulation cycle [Adamatti et al. 2007]. It also innovated in the possible replacement

of human players by artificial players.

2.3 Game Objectives

Current SimParc game has an epistemic objective: to help each participant

discover and understand the various factors, conflicts and the importance of dialogue

for a more effective management of parks. Note that this game is not (or at least not

yet) aimed at decision support (i.e., we do not expect the resulting decisions to be

directly applied to a specific park).

The game is based on a negotiation process that takes place within the park

council. This council, of a consultative nature, includes representatives of various

stakeholders (e.g., community, tourism operator, environmentalist, nongovernmental

association, water public agency…). The actual game focuses on a discussion within

the council about the “zoning” of the park, i.e. the decision about a desired level of

conservation (and therefore, use) for every sub-area (also named “landscape unit”) of

the park. We consider nine pre-defined potential levels (that we will consider as

types) of conservation/use, from more restricted to more flexible use of natural

resources, as defined by the (Brazilian) law. Examples are: Intangible, the most

conservative use, Primitive and Recuperation.

The game considers a certain number of players’ roles, each one representing a

certain stakeholder. Depending on its profile and the elements of concerns in each of

the landscape units (e.g., tourism spot, people, endangered species…), each player

will try to influence the decision about the type of conservation for each landscape

unit. It is clear that conflicts of interest will quickly emerge, leading to various

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strategies of negotiation (e.g., coalition formation, trading mutual support for

respective objectives, etc).

A special role in the game is the park manager. He is a participant of the game, but

as an arbiter and decision maker, and not as a direct player. He observes the

negotiation taking place between players and takes the final decision about the types

of conservation for each landscape unit. His decision is based on the legal framework,

on the negotiation process between the players, and on his personal profile (e.g., more

conservationist or more open to social concerns) [Irving 2006]. He may also have to

explain his decision, on players demand. Players and the park manager roles may be

played by humans or by artificial agents.

2.4 Game Cycle

The game is structured along six steps, as illustrated in Figure 1. At the beginning

(step 1), each participant is associated to a role. Then, an initial scenario is presented

to each player, including the setting of the landscape units, the possible types of use

and the general objective associated to his role. Then (step 2), each player decides a

first proposal of types of use for each landscape unit, based on his/her understanding

of the objective of his/her role and on the initial setting. Once all players have done

so, each player’s proposal is made public. In step 3, players start to interact and to

negotiate on their proposals. This step is, in our opinion, the most important one,

where players collectively build their knowledge by means of an argumentation

process. In step 4, they revise their proposals and commit themselves to a final

proposal for each landscape unit. In step 5, the park manager makes the final

decision, considering the negotiation process, the final proposals and also his

personal profile (e.g., more conservationist or more sensitive to social issues). Each

player can then consult various indicators of his/her performance (e.g., closeness to

his initial objective, degree of consensus, etc.). He can also ask for an explanation

about the park manager decision rationales. The last step (step 6) “closes” the

epistemic cycle by considering the possible effects of the decision. In the current

game, the players provide a simple feedback on the decision by indicating their level

of acceptance of the decision. A new negotiation cycle may then start, thus creating a

kind of learning cycle. The main objectives are indeed for participants: to understand

the various factors and perspectives involved and how they are interrelated; to

negotiate; to try to reach a group consensus; and to understand cause-effect relations

based on the decisions.

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Figure 1. The six steps of SimParc game.

2.5 Towards Evaluating the Viability of Decisions

As described in previous section, the last step of the game “closes” the cycle by

considering the possible effects of the decision. In the current game, players provide a

simple feedback on the decision by selecting their level of acceptance of the decision.

In a future project, we would like to introduce some technical evaluation of the

quality and viability of the decision (e.g., considering the survival of an endangered

species). Therefore, we plan to identify cases of usage conflicts (e.g., between

tourism and conservation of an endemic species) and model the dynamics of the

system (in an individual-based/multi-agent model or/and in an aggregated model).

We would then like to explore the use of viability theory to evaluate the viability of

the decision. Note that in our project current stage, we are concerned with credibility

and not yet with realism because our objective is epistemic and not about producing

an (hypothetical) optimal decision.

3. Computer Support for Role Playing Games

Our current prototype benefited from our previous experiences (game sessions and

a first prototype) and has been based on a detailed design process. Based on the

system requirements, we adopted Web-based technologies (more precisely J2E and

JSF) that support the distributed and interactive character of the game as well as an

easy deployment. Figure 2 shows the general architecture and communication

structure of SimParc prototype version 2. In this second prototype, distributed users

(the players and the park manager) interact with the system mediated internally by

communication broker agents (CBA). The function of a CBA is to abstract the fact

that each role may be played by a human or by an artificial agent. A CBA also

translates user messages in http format into multi-agent KQML format and vice

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versa. For each human player, there is also an assistant agent offering assistance

during the game session.

During the negotiation phase (see in Figure 3 an example of the corresponding

user interface [Vasconcelos et al. 2008]), players (human or artificial) negotiate

among themselves to try to reach an agreement about the type of use for each

landscape unit (sub-area) of the park. A Geographical Information System (GIS)

offers to users different layers of information (such as flora, fauna and land

characteristics) about the park geographical area. All the information exchanged

during negotiation phase namely users’ logs, game configurations, game results and

general management information are recorded and read from a PostgreSql database.

The game and the supporting prototype have been tested through two game

sessions by expert players (including a professional park manager) in January 2009

(see Figure 4).

Figure 2. SimParc version 2 general architecture

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Figure 3. Example of SimParc negotiation graphical user interface

Figure 4. SimParc session (January 2009)

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4. Inserting Artificial Agents in the SimParc Game

We are currently inserting artificial agents into the prototype. We consider three

types of artificial agents: the park manager, the artificial players and the assistants.

The park manager acts as an arbitrator in the game, making a final decision for

types of conservation for each landscape unit and explains its decision to all players.

He may be played by a human or by an artificial agent. We have implemented a

prototype of an artificial park manager, based on 2 steps: (1) internal/individual

decision by the park manager, based on some argumentation model; (2) merging of

the decision by the manager with the votes by the players, based on decision theory

(social choice). Traces of argumentation may be used for explaining the rationale of

the decision. The artificial park manager architecture is detailed in next section.

Concerning artificial players, we refer to previous experience about virtual players

in the ViP-JogoMan system [Adamatti et al., 2007]. The idea is to possibly replace

some of the human players by artificial players (artificial agents). The two main

motivations are: (1) the possible absence of sufficient number of human players for a

game session and (2) the need for testing in a systematic way specific configurations

of players’ profiles. The artificial players will be developed along park manager

existing architecture. We plan to use its argumentation capabilities to generate the

negotiation process. In parallel, we also explore using automated analysis of recorded

traces of interaction between human players in order to infer models of artificial

players. In some previous work [Guyot and Honiden 2006], genetic programming had

been used as a technique to infer interaction models, but we will also intend to use

alternative induction and machine learning techniques, e.g., inductive logic

programming.

The last type of artificial agent is an intelligent assistant agent. This agent is

designed to assist a player by performing two main tasks: (1) to help participants in

playing the game, e.g.: the assistant agent tells the player when he should make

decisions; what are the phases of the game; what should be done in each phase; etc.;

(2) to help participants during the negotiations. For this second task, we would like to

avoid intrusive support, which may interfere in his decision making cognitive

process. We have selected some objectives, e.g., to identify other players' roles with

similar or dissimilar goals, which may help the human player to find possible

coalitions or conflicts. An initial prototype implementation of an assistant agent has

been implemented.

Further details about SimParc artificial players and assistant agents may be found

in [Briot et al. 2008] and in future publications. Some advanced interface has also

been designed for human players dialogue and negotiation support and is detailed in

[Vasconcelos et al. 2008]. Now we will detail the rationale and architecture of our

automated park manager who makes the final decision.

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5. Park Manager Artificial Agent

In this section, we propose an agent architecture to implement park manager

cognitive decision rationale. As we summarized before, our decision model is based

on two mechanisms. These mechanisms could be viewed as modules of decision

subprocesses. We believe that complex decision making is achievable by sequential

organization of these modules. Before proceeding to the description of our agent

architecture, we present some more detailed motivation for it.

5.1 Objectives

Participatory management aims to emphasize the role of local actors in managing

protected areas. However, the park manager is the ultimate arbiter of all policy on

devolved matters. He acts like an expert who decides on validity of collective

concerted management policies. Moreover, he is not a completely fair and objective

arbiter: he still brings his personal opinions and preferences in the debate. Therefore,

we aim to develop an artificial agent modeling the following behaviors.

Personal preferential profile; park manager decision-making process is supposed to

be influenced by its sensibility to natural park stakes and conflicts. In decision theory

terms, we can affirm that park manager’s preferential profile could be intended as a

preference relation over conservation policies. One of the key issues is to understand

that we cannot define a strict bijection between preferential profile and preference

relation. Agent’s preference relation is partially dependent on natural park resources

and realities. Moreover, this relation is not likely to be an order or a preorder. Hence,

our agent must be able to generate its preference relation according with its

preferential profile. We distinguish two preferential profiles:

i. Preservationist, aims to preserve ecosystems and the natural environment.

ii. Socio-conservationist, generally accepts the notion of sustainable yield - that

man can harvest some forest or animal products from a natural environment

on a regular basis without compromising the long-health of the ecosystem.

Taking into account stakeholders’ decisions; a participatory decision-making leader

seeks to involve stakeholders in the process, rather than taking autocratic decisions.

However, the question of how much influence stakeholders are given may vary on

manager’s preferences and beliefs. Hence, our objective is to model the whole

spectrum of participation, from autocratic decisions to fully democratic ones. To do

so, we want the park manager agent to generate a preference preorder over

conservation policies. This is because it should be able to calculate the distance

between any two conservation policies. This way, we can merge stakeholders’

preference preorders with manager’s one to establish one participatory final decision.

Autocratic/democratic manager attitude will be modeled by an additional parameter

during the merge process.

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Expert decision; the park manager’s final decision must consider legal constraints

related to environmental management; otherwise, non-viable decisions would be

presented to the players, thus invalidating game’s learning objectives. These

constraints are directly injected in the cognitive process of the agent. Hence, the agent

will determine a dynamic preference preorder over allowed levels of conservation

(according to its preferential profile).

Explaining final decision; In order to favor the learning cycle, the park manager

agent must be able to explain its final decision to the players. We can consider that

the players could eventually argue about its decision; the agent should then defend its

purposes using some kind of argumentative reasoning. Even if such cases will be

explored in future work, it is our concern to conceive a cognitive architecture which

provides a good basis for managing these situations.

5.2 Architecture Overview

Let us now present an architecture overview of the park manager agent. As

depicted in Figure 5, agent’s architecture is structured in two phases. We believe that

sequential decision-making mechanisms can model complex cognitive behaviors

along with enhanced explanation capabilities.

Figure 5. Park Manager Agent 2-steps decision process

The first decision step concerns agent’s individual decision-making process: the

agent deliberates about the types of conservation for each landscape unit. Broadly

speaking, park manager agent builds its preference preorder over allowed levels of

conservation. An argumentation-based framework is implemented to support the

decision making.

The next step of our approach consists of taking account of players’ preferences.

The result of the execution is the modified park manager decision, called agent

participatory decision, according to stakeholder’s preferences.

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5.2.1 Agent Individual Decision

Recently, argumentation has been gaining increasing attention in the multi-agent

community. Autonomous and social agents need to deliberate under complex

preference policies, related to the environment in which they evolve. Generally,

social interactions bring new information to the agents. Hence, preference policies

need to be dynamic in order to take account of newly acquired knowledge. Dung’s

work [Dung 1995] proposes formal proof that argumentation systems can handle

epistemic reasoning under open-world assumptions, usually modeled by non-

monotonic logics. Argumentation thus becomes an established approach for

reasoning with inconsistent knowledge, based on the construction and the interaction

between arguments. Recently, some research has considered argumentation systems

capabilities to model practical reasoning, aimed at reasoning about what to do

[Hulstijn and Torre 2003, Amgoud and Kaci 2004, Rahwan and Amgoud 2006]. It

is worth noticing that argumentation can be used to select arguments that support

available desires and intentions. Consistent knowledge can generate conflicting

desires. An agent should evaluate pros and cons before pursuing any desire. Indeed,

argumentative deliberation provides a mean for choosing or discarding a desire as an

intention.

We could argue that open-world assumptions don’t hold in our context. Agent’s

knowledge base isn’t updated during execution, since it’s not directly exposed to

social interactions. Knowledge base and inference rules consistency-checking

methods are, therefore, not necessary. However, one key aspect here is to conceive an

agent capable of explaining its policy making choices; our concern is to create

favorable conditions for an effective and, thus closed, learning cycle. We believe that

argumentation “tracking” represents an effective choice for accurate explanations.

Conflicts between arguments are reported to the players, following agent’s reasoning

cycle, thus enhancing user comprehension.

From this starting position, we have developed an artificial agent on the basis of

Rahwan and Amgoud’s work [Rahwan and Amgoud 2006]. The key idea is to use

argumentation system to select the desires the agent is going to pursue: natural park

stakes and dynamics are considered in order to define objectives for which to aim.

Hence, decision-making process applies to actions, i.e. levels of conservation, which

best satisfy selected objectives. In order to deal with arguments and knowledge

representation, we use first-order logic. Various inference rules were formulated with

the objective of providing various types of reasoning capability.

For example, a simple rule for generating desires from beliefs, i.e. natural park

stakes, is:

Fire Avoid_Fires, 4

where Fire (fire danger in the park) is a belief in agent’s knowledge base and

Avoid_Fires is the desire that is generated from the belief. The value 4 represents the

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intensity of the generated desire.

Examples of rules for selecting actions, i.e. level of conservation, from desires are:

Primitive Avoid_Fires, 0.4

Intangible Avoid_Fires, 0.8

where Primitive, Intangible represent levels of conservation and values 0.4, 0.8

represent their utility in order to satisfy the corresponding desire.

5.2.2 Agent Participatory Decision

Despite participatory ideals, a whole spectrum of park managers, from autocratic

to fully democratic ones, can be measured, depending on how more participatory and

democratic decision-making is operationalized. We propose a method, fitted into the

social-choice framework, in which participatory attitude is a model parameter.

In a real case scenario, a decision-maker would examine each stakeholder's

preferences in order to reach the compromise that best reflects its participatory

attitude. Our idea is to represent this behavior by weighting each player's vote

according to manager’s point of view.

Figure 6. Park Manager Agent Participatory Decision

This concept is illustrated in Figure 6. The process is structured in two phases.

Firstly, manager agent injects its own preferences into players' choices by means of

an influence function describing agent's participatory attitude. Stronger influence

translates into more autocratic managers. Secondly, modified players' choices are

synthesized, using an aggregation function, i.e. Condorcet voting method. The result

of the execution will be the agent participatory decision.

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Example. Let the following be players' choices, where > is a preference relation

(a > b means “a is preferred to b”) and A={Intangible, Primitive, Extensive} the

candidates’ set. Players’ choices are converted into numeric vectors specifying the

candidates’ rank (each column corresponds to a candidate) for each vote:

player_1: Intangible > Primitive > Extensive, v1 = ( 3, 2, 1 )

player_2: Extensive > Primitive > Intangible, v2 = ( 1, 2, 3 )

player_3: Primitive > Extensive > Intangible, v3 = ( 1, 3, 2 )

Let Manager individual decision be:

manager_individual: Extensive > Primitive > Intangible, vM = ( 1, 2, 3 )

Let the following be the influence function:

Modified player’s vectors will be :

mv1 = ‹ f ( v1(1), vM(1) ), ‹ f ( v1(2), vM(2) ), ‹ f ( v1(3), vM(3) ) › = ( 1.5, 2, 0.5 )

mv2 = ( 1, 2, 3 )

mv3 = ( 1, 3, 2 )

In order to find manager participatory decision, we apply the Choquet integral Cμ

[Choquet 1953] choosing a symmetric capacity measure μ(S) = |S|2

/ |A|2, where A is

the candidates set.

Cμ(Intangible) = 1.05, Cμ(Primitive) = 2.12, Cμ(Extensive) = 1.27

The result of the execution will then be:

manager_participatory: Primitive > Extensive > Intangible.

Further details about architecture formal background and implementation are

reported in [Sordoni, 2008] and will be in future publications.

5.3 Examples of results

Presented manager agent architecture and its first implementation were tested over

different scenarios. Tests of the park manager agent were conducted offline in

laboratory and have been validated by team experts. We are currently completing the

integration of the park manager agent into the prototype. We will then soon conduct

new session tests with human players and park manager agent.

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We report hereafter an example of explanation for manager’s decision over a

landscape unit. Let manager individual decision be the following:

Manager_individual: Intangible > Recuperation

Arguments for Intangible are:

Endangered_Species & Tropical_Forest Maximal_Protection

Intangible Maximal_Protection

Arguments for Recuperation are:

Fire & Agricolture_Activities Recover_deteriorated_zone

Recuperation Recover_deteriorated_zone

5.4 Implementation framework

The architecture presented in this paper has been implemented in Jason multi-

agent platform [Bordini and Hubner, 2007]. Besides interpreting the original

AgentSpeak(L) language, thus disposing of logic programming capabilities, Jason

also features extensibility by user-defined internal actions, written in Java. Hence, it

has been possible to easily implement aggregation methods.

6 Conclusion

In this paper, we have presented the SimParc project, a role-playing serious game

aimed at computer-based support for participatory management of protected areas.

The lack of human resources implied in RPG gaming process acts as a constraint to

the fulfillment of pedagogical and epistemic objectives. In order to guarantee an

effective learning cycle, park manager role must be played by a domain expert.

Required expertise obviously narrows game’s autonomy and limits its context of

application. Our solution to this problem is to insert artificial agents into the game. In

this paper, we focused on the architecture to implement park manager cognitive

decision rationale. It can justify its behavior and generate a participatory decision.

Our argumentation system is based on [Amgoud and Kaci 2004, Rahwan and

Amgoud 2006]: conflicts between arguments can be reported thus enhancing user

comprehension. Moreover, we presented a decision theory framework responsible for

generating a participatory decision. To the best of our knowledge and belief, this

issue has not yet been addressed in the literature. The game and the supporting

prototype have already been tested through game sessions by expert players

(including a professional park manager) in January 2009. The final integration of the

complete SimParc prototype (with artificial agents) is under completion and we will

soon start to test it. Besides the project specific objectives, we also plan to study the

possible generality of our prototype for other types of human based social

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simulations. In the current architecture of the artificial park manager, only static

information about the park and about the votes of the players are considered. We are

considering exploring how to introduce dynamicity in the decision model, taking into

account the dynamics of negotiation among the players (the evolution of player’s

decisions during negotiation).

Acknowledgments

We thank the other current members of the project: Isabelle Alvarez, Gustavo Melo,

Altair Sancho and Davis Sansolo for their current participation; and also: Ivan

Bursztyn and Paul Guyot for their past participation. This research is funded by: the

ARCUS Program (French Ministry of Foreign Affairs, Région Ile-de-France and

Brazil) and the MCT/CNPq/CT-INFO “Grandes Desafios” Program (Project No

550865/2007-1). Some additional individual support is provided by Alßan (Europe),

CAPES and CNPq (Brazil) fellowship programs.

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